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PMLE Practice Question: A data science team uses TFX to train and deploy…
A data science team uses TFX to train and deploy a model on Vertex AI. They want automated monitoring for pipeline health. Which set of metrics should they monitor to quickly detect issues in the training pipeline?
⚠ Common exam trap
Watch out — candidates often confuse serving endpoint metrics (like latency and error rate) with pipeline health metrics, because both are part of an ML system, but the question explicitly asks about the training pipeline, not the serving infrastructure.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Pipeline execution status (success/failure), component completion times, and data validation anomalies.
The question specifically asks about monitoring the training pipeline's health, not the serving infrastructure. Pipeline execution status directly indicates whether the pipeline ran successfully, component completion times help identify bottlenecks or failures, and data validation anomalies catch data quality issues early in the pipeline — all of which are essential for detecting issues in the training pipeline itself.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Prediction request count, latency, and error rate on the serving endpoint.
Why it's wrong here
Request count, latency, and error rate describe the serving endpoint's runtime behaviour, not the training pipeline; they cannot detect upstream data or transformation failures. TFX pipeline health relies on example statistics, schema anomalies, and artefact lineage. These metrics are correct for monitoring a deployed prediction service.
- ✓
Pipeline execution status (success/failure), component completion times, and data validation anomalies.
Why this is correct
TFX pipeline health hinges on orchestration-level signals, not model accuracy. Execution status catches failed runs, component completion times expose bottlenecks or stalls in individual steps, and data validation anomalies flag schema or distribution problems before training proceeds, directly satisfying the requirement to detect training pipeline issues quickly.
- ✗
Number of pipeline runs, average CPU utilization, and memory usage.
Why it's wrong here
Run counts, CPU, and memory describe infrastructure utilisation, not pipeline correctness or data health; they cannot reveal schema drift, failed transformations, or stale training data. TFX surfaces pipeline health through artefacts and example statistics. These metrics suit capacity planning and autoscaling, not detecting training-pipeline faults.
- ✗
Model accuracy, precision, and recall on the evaluation dataset.
Why it's wrong here
Accuracy, precision, and recall measure model quality on an evaluation set, not pipeline health; a pipeline can fail silently while a stale model still scores well. TFX pipeline monitoring tracks example statistics, schema anomalies, and artefact lineage. These metrics belong to model evaluation and validation, not operational pipeline monitoring.
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